Fig. REF  shows the distributions for the two models and Table REF  reports the corresponding accuracy and entropy values. We sampled a subset of the distributions for all the object classes.
It can be observed that after the first epoch CapsNet is clearly far from optimality, both in term of performance and parse tree explainability: indeed all coupling coefficients are almost all equals to the value selected at initialization, i.e. \(1/J\) .
This effect is almost the same when we train a CapsNet with \(t=1\)  as Gu et al. [1]}, where its entropy is exactly zero but capsules are uniformly
coupled.
On the other hand, a well trained CapsNet achieves higher generalization but its entropy is getting higher, so the routing mechanism is difficult to interpret and to explain. As a matter of fact, we can see in Table REF  the dictionary for the \(\text{CapsNet}_{190}\text{+Q}\) : the number of keys for each class is too high to make the routing algorithm explainable. Notice that the MNIST test set has 10k images and the total number of keys is 8147.
We target a model that has high generalization but a dictionary where number of keys is low.
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